Sr. Data Scientist (Global Media Agency)

Analytics Lead – AI & Decision Intelligence

Technical Leadership | People Management | Client Engagement | AI / Data / ML

About the Role

Our client is an enterprise AI and Decision Intelligence product company building advanced solutions for banking,

financial services and insurance. Its work spans predictive analytics, Machine Learning, Generative AI and Agentic AI,

with a strong focus on production-ready deployment, explainability, governance and measurable business outcomes.

The company is looking for an experienced Analytics Lead – AI & Decision Intelligence to lead a multidisciplinary team

of Data Scientists, ML Engineers, AI Engineers and other technical specialists.

This is a technical leadership, people management and client-facing role. The successful candidate is not expected to

be a hands-on expert in every area of AI, Data Science and Machine Learning. Instead, the ideal person will bring deep

expertise in at least one core AI/Data/ML discipline, together with the technical breadth and managerial capability to

lead specialists across multiple domains.

You will provide direction to technical teams, translate business problems into AI-led solutions, drive successful

delivery, and act as a key interface between highly technical teams and clients. A critical part of the role is the ability to

simplify complex technical concepts and communicate them clearly to senior and non-technical stakeholders.

What You’ll Do

 Lead and manage a multidisciplinary team of Data Scientists, ML Engineers, AI Engineers and Analytics

professionals.

 Provide technical direction across AI, Machine Learning and Data Science initiatives while enabling specialists to

own their areas of expertise.

 Translate business and customer requirements into appropriate analytical and AI solution approaches.

 Own delivery across scope, priorities, timelines, technical quality and business outcomes.

 Review and challenge technical approaches, solution designs and architecture without needing to be the hands-on

specialist in every area.

 Mentor, develop and motivate technical team members and build a strong culture of collaboration and

accountability.

 Establish and reinforce best practices around model development, deployment, governance, monitoring and

continuous improvement.

 Work closely with Product, Engineering, Sales and Customer Success teams on solution development and product

roadmaps.

 Lead client workshops, technical discussions and solution presentations.

 Explain complex AI and analytics concepts in simple, business-friendly language to senior and non-technical

stakeholders.

 Support pre-sales, solutioning and customer-facing technical engagements.

Technical Capabilities

Candidates should have strong exposure to the broader AI and Data Science ecosystem. Relevant capabilities include:

 Machine Learning & Deep Learning

 Statistical Modeling

 Python and SQL

 Data Science frameworks such as Scikit-learn, XGBoost, LightGBM, TensorFlow or PyTorch

 Data Engineering concepts and Feature Engineering

 Predictive and Prescriptive Analytics

 Model Explainability

 MLOps, CI/CD, model deployment and monitoring

 Generative AI and Large Language Models (LLMs)

 Retrieval-Augmented Generation (RAG)

 AI Agents and Agentic AI architectures

Analytics Lead – AI & Decision Intelligence | LinkedIn Job Description

 Prompt Engineering

 Responsible AI, model governance and regulatory considerations

 Cloud platforms such as AWS, Azure or Google Cloud

Important: You do not need to be an expert in all of the above. We are looking for deep expertise in at least one core

area of AI, Data Science, Machine Learning, ML Engineering, Generative AI, MLOps or Data Engineering, combined with

sufficient breadth to effectively lead experts in adjacent areas.

What We’re Looking For

 5–8 years of overall experience in AI, Data Science, Machine Learning, Analytics, Data Engineering or a related

technology discipline.

 Proven experience leading or managing teams of Data Scientists, ML Engineers, AI Engineers or similar technical

professionals.

 Strong people management, mentoring, delegation and team-building capabilities.

 Experience leading complex technical projects involving multiple specialist disciplines.

 Strong client-facing, stakeholder management and project delivery skills.

 Ability to communicate technical concepts clearly to non-technical audiences.

 Strong presentation skills and confidence engaging senior business stakeholders.

 Ability to balance technical considerations with business priorities and delivery objectives.

 Strong problem-solving, decision-making, organisational and managerial skills.

Preferred Experience

 Experience delivering AI or analytics solutions in Banking, Financial Services or Insurance.

 Exposure to use cases such as credit risk, fraud detection, customer acquisition, marketing analytics, collections,

deposits or pricing analytics.

 Experience working in an AI product, technology consulting or enterprise analytics environment.

 Exposure to Generative AI, LLM and Agentic AI programmes.

 Understanding of Responsible AI, explainability and regulatory considerations.

Education

 Bachelor’s or Master’s degree in Engineering, Computer Science, Data Science, Statistics, Mathematics or a

related discipline.

 MBA preferred, but not essential.

The Profile We’re Looking For

We are looking for a technical leader who can lead experts — not someone expected to be the expert in everything.

Technical Depth + People Leadership + Client Management + Delivery Ownership

If you have strong expertise in one area of AI/Data/ML, have successfully managed technical teams, and can

confidently engage with both technologists and business leaders, we would like to hear from you.

Job Summary:

We are looking for a skilled Data Scientist with strong expertise in demand forecasting, predictive analytics, and emerging Generative AI technologies. The ideal candidate should have hands-on experience in machine learning, deep learning, NLP, and LLM-based solutions, along with proficiency in Python, SQL, Power BI, and advanced Excel. This role involves building scalable forecasting models and leveraging AI/GenAI to deliver actionable business insights.

Key Responsibilities:

• Develop and deploy demand forecasting models using machine learning and deep learning techniques.
• Analyze historical data to identify trends, seasonality, and demand patterns.
• Build predictive models to improve supply chain and inventory planning.
• Work with large datasets using Python and SQL for data extraction, transformation, and analysis.
• Design dashboards and reports using Power BI for business stakeholders.
• Utilize advanced Excel techniques (Pivot Tables, Power Query, formulas) for analysis and reporting.
• Build and integrate NLP-based solutions for text data analysis and insights.
• Develop and implement LLM-based applications using Generative AI frameworks.
• Design and deploy RAG (Retrieval-Augmented Generation) pipelines for intelligent data retrieval and response generation.
• Collaborate with cross-functional teams (operations, finance, product) to align forecasting and AI solutions.
• Continuously improve model accuracy and performance through experimentation and optimization.

Required Skills:

• Strong proficiency in Python (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch).
• Solid understanding of machine learning & deep learning algorithms.
• Experience in demand forecasting / time-series analysis (ARIMA, Prophet, LSTM, etc.).
• Hands-on experience with NLP techniques and libraries (NLTK, SpaCy, Transformers).
• Experience working with LLMs and Generative AI frameworks (OpenAI, Hugging Face, LangChain, etc.).
• Strong understanding of RAG architectures and vector databases (FAISS, Pinecone, etc.).
• Advanced knowledge of SQL for data manipulation.
• Hands-on experience with Power BI for visualization and reporting.
• Expertise in advanced Excel (Power Query, dashboards, data modeling).
• Strong analytical and problem-solving skills. 

Preferred Qualifications:

• Experience in supply chain, logistics, or e-commerce forecasting.
• Knowledge of cloud platforms (AWS, Azure, or GCP).
• Familiarity with data pipelines and ETL processes.
• Understanding of business metrics and KPIs related to demand planning.

Job Description:

As a Data Science Intern, you will collaborate with our data science and analytics teams to work on meaningful projects involving data analysis, predictive modeling, and statistical modeling. You will have the opportunity to apply your academic knowledge in a practical, fast-paced environment, contribute to key data-driven projects, and gain valuable experience with industry-leading tools and technologies.

Responsibilities:

• Assist in collecting, cleaning, and preprocessing data from various sources.
• Perform exploratory data analysis to identify trends, patterns, and anomalies.
• Develop and implement machine learning models and algorithms.
• Create data visualizations and reports to communicate findings to stakeholders.
• Collaborate with team members on data-driven projects and research.
• Participate in meetings and contribute to discussions on project progress and strategy.
• Work with large datasets to clean, preprocess, and analyze data.
• Build and deploy statistical and machine learning models to generate actionable insights.
• Conduct exploratory data analysis (EDA) to uncover trends, patterns, and correlations.
• Assist in the creation of data visualizations and dashboards for reporting insights.
• Support the development and improvement of data pipelines and algorithms.
• Collaborate with cross-functional teams to understand data needs and translate them into actionable analytics solutions.
• Contribute to the documentation and presentation of results, findings, and recommendations.
• Participate in team meetings, brainstorming sessions, and project discussions.

Duration: 03 Months (with the possibility of extending up to 6 months)

MODE: Work From Home (Online)

Requirements:

• Any Graduate / PassOuts / Freasher can apply.
• Currently pursuing a Bachelor's or Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or a related field.
• Proficiency in programming languages such as Python, R, or SQL.
• Strong foundation in statistics, probability, and data analysis techniques.

Benefits

Internship Certificate

Letter of recommendation

Stipend Performance Based

Part time work from home (2-3 Hrs per day)

5 days a week, Fully Flexible Shift

Job Title: Senior Data Analyst

Location: Sharnam Metrolinks, Amraiwadi, Ahmedabad

Working Days: 6 Days a Week

Department: MDO

Reporting To: COO

Employment Type: Full-Time

Job Summary

We are seeking a highly analytical and detail-oriented Senior Data Analyst to lead data management, reporting, automation, and system development initiatives across the organization.

The role focuses on transforming raw data into meaningful insights, designing and implementing automated systems, strengthening data structures, and supporting cross-functional teams with accurate, actionable intelligence to drive strategic and operational business decisions.

Key Responsibilities

1. Data Management & Reporting

● Collect, clean, validate, and consolidate data from multiple internal and external sources.

● Prepare and deliver daily, weekly, and monthly MIS reports for management and departments.

● Design, develop, and maintain dashboards to track KPIs, performance metrics, and operational trends.

2. Database & Data Accuracy Management

● Manage and regularly update internal databases, spreadsheets, and reporting systems.

● Ensure data accuracy, consistency, integrity, and confidentiality across all platforms.

● Implement best practices for data validation, version control, and audit checks.

3. Data Analysis & Business Insights

● Analyze large and complex datasets to identify trends, patterns, gaps, and anomalies.

● Translate data findings into clear, actionable insights and recommendations to support

strategic and operational decision-making.

4. Reporting Automation, System Recommendation & Implementation

● Identify opportunities to replace manual or semi-manual processes with automated, data-driven systems.

● Design and implement automated reporting frameworks, dashboards, and data pipelines using Excel (Power Query, VBA, Macros), SQL, BI tools, and Python.

● Proactively suggest new automation tools, system enhancements, or integrations to

improve efficiency, accuracy, and scalability.

● Lead the end-to-end implementation of approved automation initiatives, including requirement gathering, system design, testing, deployment, and stabilization.

● Continuously monitor and optimize automated systems in line with business growth and evolving data needs.

5. Cross-Functional Coordination & Support

● Collaborate with Sales, HR, Finance, Operations, and other departments to understand

reporting and data requirements.

● Provide support for ad-hoc analysis, custom reports, and special data requests.

● Act as a data partner to department heads for decision support and performance tracking.

6. Documentation & Compliance

● Maintain complete and updated documentation for MIS processes, reports, data models,

automation logic, and system changes.

● Ensure compliance with company data governance policies and applicable data protection standards.

System Development & Data Structuring Responsibilities

● Study and understand departmental workflows to evaluate how data is generated, processed, and utilized.

● Review existing manual and digital data systems to identify operational gaps, risks, and

mprovement opportunities.

● Recommend structured data models, reporting formats, and storage solutions aligned with

business requirements.

● Coordinate with department heads to define data structures, access levels, and reporting

standards.

● Implement new or upgraded data systems (Excel-based models, cloud platforms, ERP

integrations) with minimal operational disruption.

● Design structured data formats and role-based access controls to ensure secure and

organized data management.

● Train employees on newly implemented systems and provide post-implementation

support.

● Monitor system performance, resolve issues, and continuously improve systems based on

user feedback and organizational growth.

Qualifications & Experience

● Bachelor’s degree in Commerce, Statistics, Computer Applications, or a related field.

● 3–5 years of experience in data analytics, reporting, or system automation roles.

● Strong analytical thinking, logical reasoning, and problem-solving abilities.

● High attention to detail with excellent organizational and documentation skills.

Technical Skills (Must Have)

● Advanced Excel: Power Query, Power Pivot, VBA basics, Macros, Charts

● BI Tools: Power BI or Tableau (DAX, data modeling, dashboard optimization)

● Google Apps Script: Automation in Google Sheets, custom functions, triggers, API integrations, workflow optimization

● Vlook

Preferred Skills

● Strong verbal and written communication skills

● Ability to handle sensitive and confidential information responsibly

● Quick learner with a tech-savvy, improvement-driven, and solution-oriented mindset

Strong Data Analyst Profile with Python, SQL, and data-pipeline expertise, ideally with retail / e-commerce datasets

2

Mandatory (Experience 1): Must have 4+ years of hands-on data-analyst experience, working with large datasets across areas such as sales, inventory, pricing, promotions, and customer behaviour.

3

Mandatory (Tech skill 1): Must have strong hands-on Python for data processing, transformation, automation, and analysis

4

Mandatory (Tech skill 2): Must have strong SQL skills, including complex queries, joins, aggregations, and data validation.

5

Mandatory (Tech skill 3): Must have a solid understanding of Data Lake architecture, ingestion, storage, and processing workflows

6

Mandatory (Tech skill 4): Must have hands-on experience with ETL / ELT pipelines, data integration, and transformation

7

Mandatory (Tech skill 5): Must have experience with Azure or GCP data services.

8

Mandatory (Tech skill 6): Must have understanding of or experience with Databricks and distributed data processing.

9

Mandatory (Tech skill 7): Must have knowledge of data modeling, data warehousing, and Lakehouse architecture

10

Mandatory (Tech skill 8): Must have experience with Power BI, Tableau, or Excel for reporting.

11

Mandatory (Communication): Must have strong analytical, documentation, and stakeholder-communication skills.

12

Mandatory (Education): Must hold a Bachelor’s degree in Computer Science, IT, Engineering, or a related field.

13

Mandatory (Note1): This is a 6-month contract role. Candidate must be comfortable with the contract engagement and able to join immediately (urgent — onboarding within ~a week)

Role Overview 

As a Data Scientist, you will work with business stakeholders, AI engineers, and domain experts to transform data into actionable insights and intelligent solutions. You will develop machine learning models, perform statistical analysis, and contribute to AI-driven products that create measurable business impact. 

Key Responsibilities 

Data Science & Machine Learning 

• Analyze structured and unstructured data to identify patterns, trends, and business opportunities.  
• Perform exploratory data analysis (EDA), feature engineering, and data preparation.  
• Develop, evaluate, and optimize machine learning models for prediction, classification, clustering, and forecasting.  
• Apply statistical techniques to solve business problems and validate model performance.  
• Design and execute experiments to improve model accuracy and business outcomes.  

 AI Solution Development 

• Collaborate with AI Engineers, Data Engineers, and domain experts to build AI-powered solutions.  
• Translate business requirements into scalable data science approaches.  
• Contribute to Generative AI and advanced analytics initiatives where applicable.  
• Document methodologies, model performance, and key findings.  

 Required Technical Skills 

• Strong programming skills in Python and SQL for data analysis, feature engineering, and machine learning.  
• Strong understanding of Statistics, Probability, Linear Algebra, and Calculus as applied to machine learning and data science.  
• Experience with Exploratory Data Analysis (EDA), data preprocessing, feature engineering, feature selection, and handling missing or imbalanced data.  
• Good understanding of Supervised, Unsupervised, and Ensemble Machine Learning algorithms, including their assumptions, strengths, limitations, and appropriate use cases.  
• Strong knowledge of Regression, Classification, Clustering, Time Series Forecasting, Dimensionality Reduction, Recommendation Systems, and Anomaly Detection techniques.  
• Experience with Model Evaluation, Cross-Validation, Hyperparameter Optimization, Bias-Variance Trade-off, Feature Importance, Explainable AI (XAI), and Performance Metrics.  
• Understanding of Statistical Inference, Hypothesis Testing, Probability Distributions, Sampling Techniques, Confidence Intervals, and A/B Testing.  
• Experience translating business problems into analytical approaches and developing scalable, data-driven solutions.  
• Working knowledge of Generative AI, Large Language Models (LLMs), Prompt Engineering, and Retrieval-Augmented Generation (RAG) is preferred. 

Preferred Qualifications 

• Bachelor's or master's degree in computer science, Artificial Intelligence, Data Science, Statistics, Mathematics, Engineering, or a related field.  
• 2–4 years of experience developing machine learning or data science solutions.  
• Experience working on end-to-end data science projects in a business environment.  

 Nice to Have 

• Exposure to Generative AI, LLMs, RAG, or Agentic AI.  
• Experience with Computer Vision or Natural Language Processing (NLP).  
• Familiarity with cloud-based AI platforms.  
• Knowledge of construction, engineering, manufacturing, or industrial domains.  
• Participation in hackathons, research, Kaggle competitions, or open-source projects.  

 Soft Skills 

Strong analytical and problem-solving skills, effective communication and collaboration, ownership mindset, adaptability, continuous learning, and a passion for innovation.

We are looking for a hands-on Lead Data Scientist with strong analytical, machine learning, and problem-solving skills to work in a client-facing environment.

The role requires someone who can independently identify opportunities, formulate hypotheses, design solutions, and drive initiatives from analysis through experimentation and production implementation. The candidate should also be comfortable guiding team members and working closely with engineering and client stakeholders.

Responsibilities: 

• Analyse complex datasets to identify patterns, issues, and opportunities.
• Formulate and validate hypotheses through structured analysis and experimentation.
• Build and improve machine learning and anomaly detection solutions.
• Design end-to-end solutions considering data, modelling, engineering, and production constraints.
• Perform root cause analysis across models, data, and systems.
• Work with engineering teams on feature pipelines, model inference, and production deployment.
• Lead technical discussions with clients and communicate recommendations, trade-offs, and expected impact.
• Guide team members on analysis, modelling, and solution design.
• Proactively identify initiatives and roadmap items that can add value to the project.

Requirements: 

• Strong foundation in statistics and machine learning.
• Strong hands-on experience with Python and SQL.
• Hands-on experience with Python ML and data frameworks such as Pandas, NumPy, scikit-learn, TensorFlow/Keras, Dask, Matplotlib, Boto3 SageMaker Python SDK, and Horovod.
• Strong data analysis, hypothesis-generation, and problem-solving skills.
• Experience with standard supervised and unsupervised ML techniques.
• Experience with anomaly detection techniques such as Isolation Forest and Autoencoders.
• Experience building and deploying production ML solutions.
• Working knowledge of data engineering and real-time / batch inference environments.
• Strong communication and stakeholder-management skills.
• Ability to lead technical work and guide cross-functional teams.

Good to Have: 

• Experience in fraud detection or risk modelling.
• Experience with Graph Neural Networks.
• Exposure to real-time systems, streaming features, and low-latency data stores.
• Experience in AdTech, e-commerce, gaming, mobile applications, or similar high-volume consumer platforms.

About the Company: 

WebMD Health Corp., an Internet Brands Company, is the leading provider of health information services, serving patients, physicians, health care professionals, employers, and health plans through our public and private online portals, mobile platforms, and health-focused publications. The WebMD Health Network includes WebMD Health, Medscape, Jobson Healthcare Information, prIME Oncology, MediQuality, Frontline, QxMD, Vitals Consumer Services, MedicineNet, eMedicineHealth, RxList, OnHealth, Medscape Education, and other owned WebMD sites. WebMD®, Medscape®, CME Circle®, Medpulse®, eMedicine®, MedicineNet®, theheart.org®, and RxList® are among the trademarks of WebMD Health Corp. or its subsidiaries. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.

For Company details, visit our website: www.webmd.com

Experience: 3+ years

Qualification: Master's degree in Computer Science, Statistics, Mathematics, or a related field.

Work Timings: 2 PM to 11 PM IST

Position Requirement:

• Write clean and efficient code in Python and/or R for data analysis, statistical modeling, and machine learning tasks.
• Apply statistical and machine learning techniques to analyze large datasets and extract meaningful patterns, trends, and insights. Develop and implement predictive models for various business applications.
• Formulate hypothesis tests and design experiments to validate assumptions, measure the impact of changes, and optimize decision-making processes.
• Demonstrate strong SQL skills for efficient data querying and manipulation using window functions.
• Work with large datasets and ensure data quality and accuracy.
• Experience with dashboarding tools like Tableau is a plus to visualize and present data-driven insights to both technical and non-technical stakeholders.
• Ability to communicate complex technical concepts and insights in a clear and concise manner to both technical and non-technical audiences. Engage with the leadership team and clients to understand business requirements and provide data-driven solutions.
• Work with real world case studies in Data Science and a chance to implement various modelling techniques. Opportunity to independently execute and lead analytical projects and assignments.
• Help solve some challenging Healthcare related digital marketing problems globally. Transform business question into data requirements; collect and merge the data; analyse the data, link it to the business reality and present the results
• Develop predictive models and machine learning algorithms to study change in physician prescribing behaviour as well as WebMD integrated campaign response behaviour. Build analysis to understand user engagement and behaviours across various WebMD products.

Technical Skills needed:

1.Experience with data manipulation in SQL environment is a must. Experience with BigQuery/Vertica data warehousing is good to have

• Experience with statistical and data manipulation tools such as Python or R is a must 
• Need very good expertise in using Microsoft Excel, and Microsoft PowerPoint. Excellent presentation skills required
• Experience developing statistical models like hypothesis testing, regression models, classifications models, forecasting etc is a must 
• Algorithm designing and implementation skills in R or Python is required 

Requirements

• B.Tech/B.E. / MSc Statistics from premier institutes with
• Minimum 80% marks in 10th and 12th
• Minimum 75% marks in graduation
• 2-5 years of relevant work experience in analytics field
• Proven experience in data science, statistical modeling, and machine learning applications.
• Strong proficiency in Python and/or R programming languages.
• Solid understanding of statistical concepts, hypothesis testing, and experimental design
• Expertise in SQL for data querying and manipulation.
• Familiarity with dashboarding tools is a plus.
• Excellent presentation, communication and interpersonal skills.

Data Science Engineer – Python, SQL, Statistics & Machine Learning

Company: Talview

Experience: 3–5 years

Location: Bengaluru, Karnataka

Work Mode: Work from Office, with some flexibility

Work Week: 5 days

About Talview

Talview builds AI-powered hiring and assessment solutions for enterprise organisations. Our products support candidate screening, interview scheduling and online exam proctoring, helping teams improve efficiency and make better-informed decisions.

Role Overview

We are looking for a Data Science Engineer with strong hands-on experience in Python, SQL, statistics and classical machine learning, along with experience working on GenAI and Large Language Models (LLMs).

You will work across data processing, statistical analysis, experimentation, model development, evaluation and production AI/ML workflows. You will collaborate with Engineering and Product teams to translate business problems into measurable data science solutions.

This role requires practical experience in classical ML and statistics, beyond working only with LLM APIs or prompt engineering.

Roles and Responsibilities

• Develop complex SQL queries for data extraction, transformation, analysis and validation.
• Use Python for data processing, exploratory data analysis, automation and reusable experimentation utilities.
• Apply statistical analysis, hypothesis testing and experimental design to evaluate business and product outcomes.
• Develop and evaluate machine learning models using appropriate metrics and clearly defined success criteria.
• Build LLM workflows involving prompting, structured outputs and comparative evaluation of models and configurations.
• Create evaluation datasets, benchmarks, sampling strategies and ground-truth validation approaches.
• Perform error analysis and root-cause analysis to identify model failures, anomalies and performance degradation.
• Analyse production data and troubleshoot data, model and pipeline issues.
• Integrate data from APIs, databases and other structured and unstructured sources.
• Collaborate with Engineering and Product teams on ML pipelines, evaluation frameworks and inference workflows.
• Research and test techniques to improve AI/ML system performance.

Required Skills and Experience

• 3–5 years of relevant experience in Data Science, Machine Learning, AI or a related field.
• Strong hands-on Python programming skills.
• Strong SQL skills for complex querying, data transformation and analysis.
• Solid understanding of statistics, hypothesis testing, experiment design and performance measurement.
• Practical experience developing and evaluating classical machine learning models.
• Experience with LLMs, prompting, structured outputs and evaluation.
• Experience defining evaluation metrics, benchmarks and error-analysis approaches.
• Ability to work with APIs, databases and structured and unstructured datasets.
• Strong analytical and problem-solving skills.
• Ability to explain technical decisions, interpret results and communicate findings to stakeholders.

Preferred Skills

• Experience designing end-to-end ML or LLM workflows.
• Experience with dataset creation, sampling, annotation and ground-truth validation.
• Experience analysing production AI/ML systems and debugging model or pipeline issues.
• Experience building reusable experimentation and evaluation frameworks.

Key Skills

Python, SQL, Data Science, Statistics, Machine Learning, Classical ML, Hypothesis Testing, Experimental Design, Model Evaluation, Exploratory Data Analysis, GenAI, Large Language Models, LLM Evaluation, Error Analysis

About the Role

We are looking for enthusiastic Data Analytics Interns to join our team remotely for a 3-month internship. This is an excellent opportunity to gain hands-on experience in data analysis, data visualization, database management, and reporting. The selected candidates will have the opportunity to work on real projects, analyze datasets, and develop their technical and analytical skills.

Responsibilities

• Assist in collecting, cleaning, organizing, and analyzing datasets.
• Work with SQL and databases for data retrieval, manipulation, and analysis.
• Use Microsoft Excel to organize data, perform calculations, and prepare reports.
• Assist in creating dashboards and visualizations using Power BI or Tableau.
• Work with Python libraries such as Pandas and NumPy for data analysis.
• Perform exploratory data analysis to identify trends, patterns, and insights.
• Prepare reports and presentations based on analytical findings.
• Assist in tracking key performance indicators (KPIs) and monitoring business data.
• Maintain accurate documentation of data analysis activities and project contributions.
• Collaborate with team members on data-related projects and analytical tasks.

Requirements

• Basic knowledge of Data Analytics and data analysis concepts.
• Understanding of SQL and database fundamentals.
• Familiarity with Microsoft Excel and data management.
• Basic knowledge of Python is preferred.
• Familiarity with data visualization tools such as Power BI or Tableau is an advantage.
• Strong analytical and problem-solving skills.
• Good research and organizational abilities.
• Attention to detail and accuracy.
• Ability to work independently in a remote environment.
• Good communication skills and eagerness to learn.

What You'll Gain

• Real-world experience in data analytics and business intelligence.
• Hands-on experience with data cleaning, analysis, and visualization.
• Exposure to industry-relevant tools such as SQL, Python, Excel, and Power BI.
• Mentorship and exposure to practical analytical processes.
• Completion Certificate at the end of the internship.
• Letter of Recommendation based on performance.
• Opportunity to strengthen your portfolio with real-world data projects.

Internship Details

• Duration: 3 months
• Location: Remote (Work from Home)
• Perks: Completion Certificate + Letter of Recommendation